1 Executive Summary

The aim of this report is to observe any effects of self-quarantines due to COVID-19 on people’s hobbies.
The main discoveries are …

2 Full Report

2.1 Initial Data Analysis (IDA)

RStudio has many data sets already loaded in. The example below uses preloaded data direct from RStudio example dataset: mtcars.

Read about the mtcars data set.

In the rmd file, you will see how you can load your own dataset from either 1) an online source using a URL or 2) a local file on your own computer.

#Load our data
survey = read.csv("HobbiesCOVID19.csv")

# Remove the Timestamp column as it is not necessary for our report
survey$Timestamp <- NULL

# Quick look at the structure of data
str(survey)

## 'data.frame':    29 obs. of  9 variables:
##  $ age           : chr  "17-23" "17-23" "17-23" "17-23" ...
##  $ gender        : chr  "Male" "Female" "Female" "Female" ...
##  $ covidhobbyno  : int  2 1 0 6 2 3 6 0 4 3 ...
##  $ covidhobbytype: chr  "Cooking/baking, Learning Japanese" "Sports/exercise, Cooking/baking" "Literature, Music, Sports/exercise, Video games, Movies/TV shows" "Literature, Music, Sports/exercise, Arts, Video games, Movies/TV shows" ...
##  $ reliance      : int  3 4 0 4 3 2 3 5 4 2 ...
##  $ continue      : chr  "Yes" "No" "Yes" "Yes" ...
##  $ nowhobbyno    : int  2 0 4 4 1 2 3 0 3 2 ...
##  $ nowhobbytype  : chr  "Cooking/baking, Learning Japanese" "" "Literature, Sports/exercise, Video games" "Music, Sports/exercise, Video games, Movies/TV shows" ...
##  $ discontinue   : chr  "" "Not enough time, Was not suitable for me/became bored" "Not enough time" "Not enough time, Bored" ...

# Quick look at top 5 rows of data
head(survey)

##     age gender covidhobbyno
## 1 17-23   Male            2
## 2 17-23 Female            1
## 3 17-23 Female            0
## 4 17-23 Female            6
## 5 17-23   Male            2
## 6 17-23 Female            3
##                                                           covidhobbytype
## 1                                      Cooking/baking, Learning Japanese
## 2                                        Sports/exercise, Cooking/baking
## 3       Literature, Music, Sports/exercise, Video games, Movies/TV shows
## 4 Literature, Music, Sports/exercise, Arts, Video games, Movies/TV shows
## 5                                       Sports/exercise, Movies/TV shows
## 6                              Music, Arts, Video games, Movies/TV shows
##   reliance continue nowhobbyno
## 1        3      Yes          2
## 2        4       No          0
## 3        0      Yes          4
## 4        4      Yes          4
## 5        3      Yes          1
## 6        2      Yes          2
##                                           nowhobbytype
## 1                    Cooking/baking, Learning Japanese
## 2                                                     
## 3             Literature, Sports/exercise, Video games
## 4 Music, Sports/exercise, Video games, Movies/TV shows
## 5                                      Movies/TV shows
## 6                               Music, Movies/TV shows
##                                             discontinue
## 1                                                      
## 2 Not enough time, Was not suitable for me/became bored
## 3                                       Not enough time
## 4                                Not enough time, Bored
## 5                                    Lack of motivation
## 6                                       Not enough time

# Our data has 29 rows and 9 columns

# Size of the data
dim(survey)

## [1] 29  9

# R's classification of survey's data
class(survey)

## [1] "data.frame"

## R's classification of variables
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

#sapply(mtcars, class)

Summary:

The data came from our survey on Google form
The data is valid because …
Possible issues include …
Each row represents …
Each column represents …

2.2 Research Question 1

How did COVID-19 affected people’s hobbies → Number of hobbies during and after covid, bar graph

# Number of hobbies picked up during quarantine
barplot(table(survey$covidhobbyno), main="Number of hobbies picked up during quarantine")

#Number of hobbies now
barplot(table(survey$nowhobbyno), main="Number of hobbies now")

Summary:

2.3 Research Question 2

What kind of hobbies people start doing during Covid?

# Install packages ggplot2
library(ggplot2)


p = ggplot(survey, aes(x=covidhobbytype))
p + geom_bar()

2.4 Research Question 3

Is there a linear relationship between the number of hobbies and people’s reliance of hobbies (number of hobbies vs comfort level)

# Construct a scatter plot
plot(survey$covidhobbyno, survey$reliance)

# Calculate the linear regression model to draw on the scatter plot
L = lm(survey$covidhobbyno ~ survey$reliance)

summary(L)

## 
## Call:
## lm(formula = survey$covidhobbyno ~ survey$reliance)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9785 -0.7450 -0.2779  0.7221  3.4885 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       1.8109     0.7267   2.492   0.0191 *
## survey$reliance   0.2335     0.2122   1.100   0.2809  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.473 on 27 degrees of freedom
## Multiple R-squared:  0.04291,    Adjusted R-squared:  0.007466 
## F-statistic: 1.211 on 1 and 27 DF,  p-value: 0.2809

L$coeff

##     (Intercept) survey$reliance 
##       1.8108883       0.2335244

abline(L)

# Caluculate the linear correlation coefficient
cor(survey$covidhobbyno, survey$reliance)

## [1] 0.2071563

Summary:

3 References

Style: APA

4 Beyond the Basics - extending your abilities with RMarkdown

This quick reference guide will cover some basic RMarkdown for use in your projects.

4.1 Lists

Here is a basic list:

To do 1
To do 2
To do 3

4.2 Links

Here is a link to interesting web page:

Interesting Web Page

4.3 Tables

Here is a simple table.

Tables	Are	Cool
col 3 is	right-aligned	$1600
col 2 is	centered	$12
zebra stripes	are neat	$1

4.4 Images

Here is am image. It has not been adjusted in the rmd file, so represents the true size of the original image. This image is sourced directly from an online url.

To learn more about adding images directly from your own computer, see the comments in this rmd file.

Image source: https://petcube.com/blog/10-all-important-kitten-supplies-infographic/

4.5 Videos

Below you will find a video embedded into your RMarkdown file. Change the YouTube link in the rmd file to get a different video.

4.6 LaTeX

You can even use LaTeX in an RMarkdown document!

For example, how could you work out $\sum_{i=1}^{5} x_{i}^3$?

4.7 R Code

Here is an R code chunk:

Try the following commands in R.

1+ exp(3) + sin(0.5)
x=c(1,2,3)
x^2
sum(x)

Here is some in-line code in-line code. You can put any R code here for display, e.g. sum(x)

5 RMarkdown Resources

Check out the resources below for more information on RMarkdown.

How to use R Markdown

Mastering Markdown

Reproducible Report

Project 2

Student/s SID

University of Sydney | DATA1001 | 2021

1 Executive Summary

2 Full Report

2.1 Initial Data Analysis (IDA)

2.2 Research Question 1

2.3 Research Question 2

2.4 Research Question 3

3 References

4 Beyond the Basics - extending your abilities with RMarkdown

4.1 Lists

4.2 Links

4.3 Tables

4.4 Images

4.5 Videos

4.6 LaTeX

4.7 R Code

5 RMarkdown Resources